Welcome to the Notebook

The COVID-19 data visualization project aims to visually represent key insights and trends related to the pandemic using Python. It involves collecting, cleaning, and analyzing COVID-19 data, then creating various visualizations such as geospatial maps, time series plots, and statistical charts. The goal is to provide a clear and interactive dashboard that enhances understanding of the virus's spread and impact.

Importing modules

Task 1

import pandas as pd
import numpy as np
import plotly.express as px
import matplotlib.pyplot as plt 
print('modules are imported')

modules are imported

Task 1.1:

Loading the Dataset

df=pd.read_csv('full_grouped.csv')

Task 1.2:

let's check the dataframe

df.head()

	Date	Country/Region	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	WHO Region
0	2020-01-22	Afghanistan	0	0	0	0	0	0	0	Eastern Mediterranean
1	2020-01-22	Albania	0	0	0	0	0	0	0	Europe
2	2020-01-22	Algeria	0	0	0	0	0	0	0	Africa
3	2020-01-22	Andorra	0	0	0	0	0	0	0	Europe
4	2020-01-22	Angola	0	0	0	0	0	0	0	Africa

df.tail()

	Date	Country/Region	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	WHO Region
35151	2020-07-27	West Bank and Gaza	10621	78	3752	6791	152	2	0	Eastern Mediterranean
35152	2020-07-27	Western Sahara	10	1	8	1	0	0	0	Africa
35153	2020-07-27	Yemen	1691	483	833	375	10	4	36	Eastern Mediterranean
35154	2020-07-27	Zambia	4552	140	2815	1597	71	1	465	Africa
35155	2020-07-27	Zimbabwe	2704	36	542	2126	192	2	24	Africa

let's check the shape of the dataframe

df.shape

(35156, 10)

Task 2.1 :

let's do some preprocessing

df = df[(df['Confirmed'] > 0) & (df['Deaths'] > 0)]

df.head()

	Date	Country/Region	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	WHO Region
36	2020-01-22	China	548	17	28	503	0	0	0	Western Pacific
223	2020-01-23	China	643	18	30	595	95	1	2	Western Pacific
410	2020-01-24	China	920	26	36	858	277	8	6	Western Pacific
597	2020-01-25	China	1406	42	39	1325	486	16	3	Western Pacific
784	2020-01-26	China	2075	56	49	1970	669	14	10	Western Pacific

let's see data related to a country for example India

df[df['Country/Region'] == 'India']

	Date	Country/Region	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	WHO Region
9242	2020-03-11	India	62	1	4	57	6	1	0	South-East Asia
9429	2020-03-12	India	73	1	4	68	11	0	0	South-East Asia
9616	2020-03-13	India	82	2	4	76	9	1	0	South-East Asia
9803	2020-03-14	India	102	2	4	96	20	0	0	South-East Asia
9990	2020-03-15	India	113	2	13	98	11	0	9	South-East Asia
...	...	...	...	...	...	...	...	...	...	...
34300	2020-07-23	India	1288108	30601	817209	440298	49310	740	34602	South-East Asia
34487	2020-07-24	India	1337024	31358	849432	456234	48916	757	32223	South-East Asia
34674	2020-07-25	India	1385635	32060	885573	468002	48611	702	36141	South-East Asia
34861	2020-07-26	India	1435616	32771	917568	485277	49981	711	31995	South-East Asia
35048	2020-07-27	India	1480073	33408	951166	495499	44457	637	33598	South-East Asia

139 rows × 10 columns

let's see Global spread of Covid19

fig = px.choropleth(df,locations = 'Country/Region' ,locationmode = 'country names' , color='Confirmed' ,animation_frame='Date')
fig.update_layout(title_text = 'Global Spread of Covid-19 from 22nd Jan 2020 to 16th July 2020')
fig.show()

Task 2.2 : Exercise

Let's see Global deaths of Covid19

fig = px.choropleth(df,locations = 'Country/Region' ,locationmode = 'country names' , color='Deaths' ,animation_frame='Date')
fig.update_layout(title_text = 'Global Deaths of Covid-19 from 22nd Jan 2020 to 16th July 2020')
fig.show()

Task 3.1:

Let's Visualize how intensive the Covid19 Transmission has been in each of the country

let's start with an example:

df_china = df[df['Country/Region'] == 'China']
df_china.head()

	Date	Country/Region	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	WHO Region
36	2020-01-22	China	548	17	28	503	0	0	0	Western Pacific
223	2020-01-23	China	643	18	30	595	95	1	2	Western Pacific
410	2020-01-24	China	920	26	36	858	277	8	6	Western Pacific
597	2020-01-25	China	1406	42	39	1325	486	16	3	Western Pacific
784	2020-01-26	China	2075	56	49	1970	669	14	10	Western Pacific

let's select the columns that we need

df_china.pop('WHO Region')           #---------Dropping WHO Region Column ----------

36       Western Pacific
223      Western Pacific
410      Western Pacific
597      Western Pacific
784      Western Pacific
              ...       
34257    Western Pacific
34444    Western Pacific
34631    Western Pacific
34818    Western Pacific
35005    Western Pacific
Name: WHO Region, Length: 188, dtype: object

df_china.head()

	Date	Country/Region	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered
36	2020-01-22	China	548	17	28	503	0	0	0
223	2020-01-23	China	643	18	30	595	95	1	2
410	2020-01-24	China	920	26	36	858	277	8	6
597	2020-01-25	China	1406	42	39	1325	486	16	3
784	2020-01-26	China	2075	56	49	1970	669	14	10

calculating the first derivation of confrimed column

px.line(df_china, x = 'Date' , y = ['Confirmed' ,'New cases'])

print("Maximum New Cases on Single day  = ",df_china['New cases'].max())

Maximum New Cases on Single day  =  13628

px.line(df_china , x = 'Date' ,y = ['Deaths' , 'New deaths'])

df_china['New deaths'].max()

1290

Task 3.2:

Let's Calculate Maximum infection rate for all of the countries

df.head()

	Date	Country/Region	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	WHO Region
36	2020-01-22	China	548	17	28	503	0	0	0	Western Pacific
223	2020-01-23	China	643	18	30	595	95	1	2	Western Pacific
410	2020-01-24	China	920	26	36	858	277	8	6	Western Pacific
597	2020-01-25	China	1406	42	39	1325	486	16	3	Western Pacific
784	2020-01-26	China	2075	56	49	1970	669	14	10	Western Pacific

Countries = list(df['Country/Region'].unique())
max_new_cases = []
for i in Countries:
    mnc = df[df['Country/Region'] == i].Confirmed.diff().max()
    max_new_cases.append(mnc)

    

Task 3.3:

let's create a new Dataframe

df_mnc = pd.DataFrame()
df_mnc['Country'] = Countries
df_mnc['Max_Cases'] = max_new_cases
df_mnc.head()

	Country	Max_Cases
0	China	13628.0
1	Philippines	2486.0
2	Japan	978.0
3	France	26849.0
4	Taiwan*	27.0

df_mnc.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 171 entries, 0 to 170
Data columns (total 2 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   Country    171 non-null    object 
 1   Max_Cases  171 non-null    float64
dtypes: float64(1), object(1)
memory usage: 2.8+ KB

Let's plot the barchart : maximum infection rate of each country

px.bar(df_mnc,x='Country',y='Max_Cases',color='Country',title = 'Global Maximum Covid Cases',log_y=True)

Let's See how National Lockdowns Impacts Covid19 transmission in Italy

COVID19 pandemic lockdown in Italy

On 9 March 2020, the government of Italy under Prime Minister Giuseppe Conte imposed a national quarantine, restricting the movement of the population except for necessity, work, and health circumstances, in response to the growing pandemic of COVID-19 in the country. source

italy_lockdown_start_date = '2020-03-09'
italy_lockdown_a_month_later = '2020-04-09'

df.head()

	Date	Country/Region	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	WHO Region
36	2020-01-22	China	548	17	28	503	0	0	0	Western Pacific
223	2020-01-23	China	643	18	30	595	95	1	2	Western Pacific
410	2020-01-24	China	920	26	36	858	277	8	6	Western Pacific
597	2020-01-25	China	1406	42	39	1325	486	16	3	Western Pacific
784	2020-01-26	China	2075	56	49	1970	669	14	10	Western Pacific

let's get data related to italy

df_italy = df[df['Country/Region'] == 'Italy']

lets check the dataframe

df_italy.head()

	Date	Country/Region	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	WHO Region
5695	2020-02-21	Italy	20	1	0	19	17	1	0	Europe
5882	2020-02-22	Italy	62	2	1	59	42	1	1	Europe
6069	2020-02-23	Italy	155	3	2	150	93	1	1	Europe
6256	2020-02-24	Italy	229	7	1	221	74	4	-1	Europe
6443	2020-02-25	Italy	322	10	1	311	93	3	0	Europe

ok! now let's do the visualization

fig = px.line(df_italy , x='Date',y='New cases',title='Before And After Lockdown in Italy')

#-------------------------------Function for Starting Date ------------------------------
fig.add_shape(
    dict(
    type='line',
    x0=italy_lockdown_start_date, y0=0,
    x1=italy_lockdown_start_date , y1=df_italy['New cases'].max(),
    line = dict(color='red',width=3)
    )

)
fig.add_annotation(
    dict(
    x=italy_lockdown_start_date,
    y=df_italy['New cases'].max(),
    text = 'Starting Date of the Month'
    )
)
#-----------------------------Function for Ending date ------------------------------
fig.add_shape(
    dict(
    type='line',
    x0=italy_lockdown_a_month_later, y0=0,
    x1=italy_lockdown_a_month_later , y1=df_italy['New cases'].max(),
    line = dict(color='red',width=3)
    )

)
fig.add_annotation(
    dict(
    x=italy_lockdown_a_month_later,
    y=df_italy['New cases'].max(),
    text = 'Ending Date of the Month'
    )
)

Task 5: Let's See how National Lockdowns Impacts Covid19 active cases in Italy

df_italy.head()

	Date	Country/Region	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	WHO Region
5695	2020-02-21	Italy	20	1	0	19	17	1	0	Europe
5882	2020-02-22	Italy	62	2	1	59	42	1	1	Europe
6069	2020-02-23	Italy	155	3	2	150	93	1	1	Europe
6256	2020-02-24	Italy	229	7	1	221	74	4	-1	Europe
6443	2020-02-25	Italy	322	10	1	311	93	3	0	Europe

now let's plot a line chart to compare COVID19 national lockdowns impacts on spread of the virus and number of active cases

fig =px.line(df_italy,x='Date',y=['New cases','New deaths'])
fig.show()

Normalizing the Columns

df_italy['Normalized_Affection_Rate']= df_italy['New cases']/df_italy['New cases'].max()
df_italy['Normalized_Death_Rate']= df_italy['New deaths']/df_italy['New deaths'].max()
df_italy.pop('WHO Region')  

C:\Users\SAURAV BEDSE\AppData\Local\Temp\ipykernel_16312\1512520190.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\SAURAV BEDSE\AppData\Local\Temp\ipykernel_16312\1512520190.py:2: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

5695     Europe
5882     Europe
6069     Europe
6256     Europe
6443     Europe
          ...  
34306    Europe
34493    Europe
34680    Europe
34867    Europe
35054    Europe
Name: WHO Region, Length: 158, dtype: object

df_italy.head()

	Date	Country/Region	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	Normalized_Affection_Rate	Normalized_Death_Rate
5695	2020-02-21	Italy	20	1	0	19	17	1	0	0.002593	0.001088
5882	2020-02-22	Italy	62	2	1	59	42	1	1	0.006405	0.001088
6069	2020-02-23	Italy	155	3	2	150	93	1	1	0.014183	0.001088
6256	2020-02-24	Italy	229	7	1	221	74	4	-1	0.011286	0.004353
6443	2020-02-25	Italy	322	10	1	311	93	3	0	0.014183	0.003264

Lets Plot Line chart

fig =px.line(df_italy,x='Date',y=['Normalized_Affection_Rate','Normalized_Death_Rate'])
fig.show()

COVID19 pandemic lockdown in Germany

Lockdown was started in Freiburg, Baden-Württemberg and Bavaria on 20 March 2020. Three days later, it was expanded to the whole of Germany

Germany_lockdown_start_date = '2020-03-23' 
Germany_lockdown_a_month_later = '2020-04-23'

let's select the data related to Germany

df_germany = df[df['Country/Region'] == 'Germany']

let's check the dataframe

df_germany.head()

	Date	Country/Region	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	WHO Region
8854	2020-03-09	Germany	1176	2	18	1156	136	2	0	Europe
9041	2020-03-10	Germany	1457	2	18	1437	281	0	0	Europe
9228	2020-03-11	Germany	1908	3	25	1880	451	1	7	Europe
9415	2020-03-12	Germany	2078	3	25	2050	170	0	0	Europe
9602	2020-03-13	Germany	3675	7	46	3622	1597	4	21	Europe

selecting the needed column

fig = px.line(df_germany , x='Date',y='New cases',title='Before And After Lockdown in germany')

#-------------------------------Function for Starting Date ------------------------------
fig.add_shape(
    dict(
    type='line',
    x0=Germany_lockdown_start_date, y0=0,
    x1=Germany_lockdown_start_date , y1=df_germany['New cases'].max(),
    line = dict(color='red',width=3)
    )

)
fig.add_annotation(
    dict(
    x=Germany_lockdown_start_date,
    y=df_germany['New cases'].max(),
    text = 'Starting Date of the Month'
    )
)
#-----------------------------Function for Ending date ------------------------------
fig.add_shape(
    dict(
    type='line',
    x0=Germany_lockdown_a_month_later, y0=0,
    x1=Germany_lockdown_a_month_later , y1=df_germany['New cases'].max(),
    line = dict(color='red',width=3)
    )

)
fig.add_annotation(
    dict(
    x=Germany_lockdown_a_month_later,
    y=df_germany['New cases'].max(),
    text = 'Ending Date of the Month'
    )
)

now let's plot the line chart

fig = px.line(df_germany,x='Date',y=['New cases','New deaths'])

fig.show()

Normalizing the New-cases & New-deaths

let's do some scaling

df_germany['Normalized_Affection_Rate']= df_germany['New cases']/df_germany['New cases'].max()
df_germany['Normalized_Death_Rate']= df_germany['New deaths']/df_germany['New deaths'].max()

C:\Users\SAURAV BEDSE\AppData\Local\Temp\ipykernel_16312\754776824.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\SAURAV BEDSE\AppData\Local\Temp\ipykernel_16312\754776824.py:2: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

let's plot the line chart

fig =px.line(df_germany,x='Date',y=['Normalized_Affection_Rate','Normalized_Death_Rate'])

fig.show()